Overview

Dataset statistics

Number of variables37
Number of observations4424
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory296.0 B

Variable types

Numeric28
Categorical9

Alerts

Application mode is highly overall correlated with Age at enrollmentHigh correlation
Course is highly overall correlated with Daytime/evening attendanceHigh correlation
Previous qualification (grade) is highly overall correlated with Admission gradeHigh correlation
Nacionality is highly overall correlated with InternationalHigh correlation
Admission grade is highly overall correlated with Previous qualification (grade)High correlation
Age at enrollment is highly overall correlated with Application modeHigh correlation
Curricular units 1st sem (credited) is highly overall correlated with Curricular units 2nd sem (credited)High correlation
Curricular units 1st sem (enrolled) is highly overall correlated with Curricular units 1st sem (approved) and 2 other fieldsHigh correlation
Curricular units 1st sem (evaluations) is highly overall correlated with Curricular units 2nd sem (evaluations)High correlation
Curricular units 1st sem (approved) is highly overall correlated with Curricular units 1st sem (enrolled) and 4 other fieldsHigh correlation
Curricular units 1st sem (grade) is highly overall correlated with Curricular units 1st sem (approved) and 2 other fieldsHigh correlation
Curricular units 2nd sem (credited) is highly overall correlated with Curricular units 1st sem (credited)High correlation
Curricular units 2nd sem (enrolled) is highly overall correlated with Curricular units 1st sem (enrolled) and 2 other fieldsHigh correlation
Curricular units 2nd sem (evaluations) is highly overall correlated with Curricular units 1st sem (evaluations)High correlation
Curricular units 2nd sem (approved) is highly overall correlated with Curricular units 1st sem (enrolled) and 5 other fieldsHigh correlation
Curricular units 2nd sem (grade) is highly overall correlated with Curricular units 1st sem (approved) and 2 other fieldsHigh correlation
Daytime/evening attendance is highly overall correlated with CourseHigh correlation
International is highly overall correlated with NacionalityHigh correlation
Target is highly overall correlated with Curricular units 2nd sem (approved)High correlation
Daytime/evening attendance is highly imbalanced (50.3%)Imbalance
Educational special needs is highly imbalanced (90.9%)Imbalance
International is highly imbalanced (83.2%)Imbalance
Mother's occupation has 144 (3.3%) zerosZeros
Father's occupation has 128 (2.9%) zerosZeros
Curricular units 1st sem (credited) has 3847 (87.0%) zerosZeros
Curricular units 1st sem (enrolled) has 180 (4.1%) zerosZeros
Curricular units 1st sem (evaluations) has 349 (7.9%) zerosZeros
Curricular units 1st sem (approved) has 718 (16.2%) zerosZeros
Curricular units 1st sem (grade) has 718 (16.2%) zerosZeros
Curricular units 1st sem (without evaluations) has 4130 (93.4%) zerosZeros
Curricular units 2nd sem (credited) has 3894 (88.0%) zerosZeros
Curricular units 2nd sem (enrolled) has 180 (4.1%) zerosZeros
Curricular units 2nd sem (evaluations) has 401 (9.1%) zerosZeros
Curricular units 2nd sem (approved) has 870 (19.7%) zerosZeros
Curricular units 2nd sem (grade) has 870 (19.7%) zerosZeros
Curricular units 2nd sem (without evaluations) has 4142 (93.6%) zerosZeros

Reproduction

Analysis started2023-11-04 14:30:37.399938
Analysis finished2023-11-04 14:31:56.579687
Duration1 minute and 19.18 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

Marital Status
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1785714
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2023-11-04T09:31:56.682414image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum6
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.60574695
Coefficient of variation (CV)0.51396711
Kurtosis21.482639
Mean1.1785714
Median Absolute Deviation (MAD)0
Skewness4.3997643
Sum5214
Variance0.36692936
MonotonicityNot monotonic
2023-11-04T09:31:56.775479image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 3919
88.6%
2 379
 
8.6%
4 91
 
2.1%
5 25
 
0.6%
6 6
 
0.1%
3 4
 
0.1%
ValueCountFrequency (%)
1 3919
88.6%
2 379
 
8.6%
3 4
 
0.1%
4 91
 
2.1%
5 25
 
0.6%
6 6
 
0.1%
ValueCountFrequency (%)
6 6
 
0.1%
5 25
 
0.6%
4 91
 
2.1%
3 4
 
0.1%
2 379
 
8.6%
1 3919
88.6%

Application mode
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.669078
Minimum1
Maximum57
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2023-11-04T09:31:56.864130image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median17
Q339
95-th percentile44
Maximum57
Range56
Interquartile range (IQR)38

Descriptive statistics

Standard deviation17.484682
Coefficient of variation (CV)0.93655844
Kurtosis-1.4538061
Mean18.669078
Median Absolute Deviation (MAD)16
Skewness0.39303572
Sum82592
Variance305.71411
MonotonicityNot monotonic
2023-11-04T09:31:56.968887image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1 1708
38.6%
17 872
19.7%
39 785
17.7%
43 312
 
7.1%
44 213
 
4.8%
7 139
 
3.1%
18 124
 
2.8%
42 77
 
1.7%
51 59
 
1.3%
16 38
 
0.9%
Other values (8) 97
 
2.2%
ValueCountFrequency (%)
1 1708
38.6%
2 3
 
0.1%
5 16
 
0.4%
7 139
 
3.1%
10 10
 
0.2%
15 30
 
0.7%
16 38
 
0.9%
17 872
19.7%
18 124
 
2.8%
26 1
 
< 0.1%
ValueCountFrequency (%)
57 1
 
< 0.1%
53 35
 
0.8%
51 59
 
1.3%
44 213
 
4.8%
43 312
 
7.1%
42 77
 
1.7%
39 785
17.7%
27 1
 
< 0.1%
26 1
 
< 0.1%
18 124
 
2.8%

Application order
Real number (ℝ)

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7278481
Minimum0
Maximum9
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2023-11-04T09:31:57.078395image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile5
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3137931
Coefficient of variation (CV)0.76036376
Kurtosis2.6512887
Mean1.7278481
Median Absolute Deviation (MAD)0
Skewness1.88105
Sum7644
Variance1.7260523
MonotonicityNot monotonic
2023-11-04T09:31:57.169247image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 3026
68.4%
2 547
 
12.4%
3 309
 
7.0%
4 249
 
5.6%
5 154
 
3.5%
6 137
 
3.1%
9 1
 
< 0.1%
0 1
 
< 0.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 3026
68.4%
2 547
 
12.4%
3 309
 
7.0%
4 249
 
5.6%
5 154
 
3.5%
6 137
 
3.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
6 137
 
3.1%
5 154
 
3.5%
4 249
 
5.6%
3 309
 
7.0%
2 547
 
12.4%
1 3026
68.4%
0 1
 
< 0.1%

Course
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8856.6426
Minimum33
Maximum9991
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2023-11-04T09:31:57.269598image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum33
5-th percentile171
Q19085
median9238
Q39556
95-th percentile9991
Maximum9991
Range9958
Interquartile range (IQR)471

Descriptive statistics

Standard deviation2063.5664
Coefficient of variation (CV)0.23299646
Kurtosis13.199149
Mean8856.6426
Median Absolute Deviation (MAD)262
Skewness-3.8091352
Sum39181787
Variance4258306.4
MonotonicityNot monotonic
2023-11-04T09:31:57.377385image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
9500 766
17.3%
9147 380
 
8.6%
9238 355
 
8.0%
9085 337
 
7.6%
9773 331
 
7.5%
9670 268
 
6.1%
9991 268
 
6.1%
9254 252
 
5.7%
9070 226
 
5.1%
171 215
 
4.9%
Other values (7) 1026
23.2%
ValueCountFrequency (%)
33 12
 
0.3%
171 215
4.9%
8014 215
4.9%
9003 210
4.7%
9070 226
5.1%
9085 337
7.6%
9119 170
3.8%
9130 141
 
3.2%
9147 380
8.6%
9238 355
8.0%
ValueCountFrequency (%)
9991 268
 
6.1%
9853 192
 
4.3%
9773 331
7.5%
9670 268
 
6.1%
9556 86
 
1.9%
9500 766
17.3%
9254 252
 
5.7%
9238 355
8.0%
9147 380
8.6%
9130 141
 
3.2%

Daytime/evening attendance
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.7 KiB
1
3941 
0
483 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4424
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 3941
89.1%
0 483
 
10.9%

Length

2023-11-04T09:31:57.480561image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-04T09:31:57.582113image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1 3941
89.1%
0 483
 
10.9%

Most occurring characters

ValueCountFrequency (%)
1 3941
89.1%
0 483
 
10.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4424
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3941
89.1%
0 483
 
10.9%

Most occurring scripts

ValueCountFrequency (%)
Common 4424
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3941
89.1%
0 483
 
10.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3941
89.1%
0 483
 
10.9%

Previous qualification
Real number (ℝ)

Distinct17
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5777577
Minimum1
Maximum43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2023-11-04T09:31:57.686494image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile39
Maximum43
Range42
Interquartile range (IQR)0

Descriptive statistics

Standard deviation10.216592
Coefficient of variation (CV)2.2317897
Kurtosis6.7781662
Mean4.5777577
Median Absolute Deviation (MAD)0
Skewness2.8712068
Sum20252
Variance104.37876
MonotonicityNot monotonic
2023-11-04T09:31:57.777634image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 3717
84.0%
39 219
 
5.0%
19 162
 
3.7%
3 126
 
2.8%
12 45
 
1.0%
40 40
 
0.9%
42 36
 
0.8%
2 23
 
0.5%
6 16
 
0.4%
9 11
 
0.2%
Other values (7) 29
 
0.7%
ValueCountFrequency (%)
1 3717
84.0%
2 23
 
0.5%
3 126
 
2.8%
4 8
 
0.2%
5 1
 
< 0.1%
6 16
 
0.4%
9 11
 
0.2%
10 4
 
0.1%
12 45
 
1.0%
14 1
 
< 0.1%
ValueCountFrequency (%)
43 6
 
0.1%
42 36
 
0.8%
40 40
 
0.9%
39 219
5.0%
38 7
 
0.2%
19 162
3.7%
15 2
 
< 0.1%
14 1
 
< 0.1%
12 45
 
1.0%
10 4
 
0.1%

Previous qualification (grade)
Real number (ℝ)

HIGH CORRELATION 

Distinct101
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean132.61331
Minimum95
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2023-11-04T09:31:57.884161image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum95
5-th percentile110
Q1125
median133.1
Q3140
95-th percentile157
Maximum190
Range95
Interquartile range (IQR)15

Descriptive statistics

Standard deviation13.188332
Coefficient of variation (CV)0.09944953
Kurtosis0.96825772
Mean132.61331
Median Absolute Deviation (MAD)7.1
Skewness0.31286749
Sum586681.3
Variance173.93209
MonotonicityNot monotonic
2023-11-04T09:31:57.996178image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
133.1 491
 
11.1%
130 375
 
8.5%
140 336
 
7.6%
120 278
 
6.3%
150 162
 
3.7%
125 122
 
2.8%
135 108
 
2.4%
110 101
 
2.3%
131 99
 
2.2%
160 95
 
2.1%
Other values (91) 2257
51.0%
ValueCountFrequency (%)
95 1
 
< 0.1%
96 2
 
< 0.1%
97 1
 
< 0.1%
99 2
 
< 0.1%
100 76
1.7%
101 6
 
0.1%
102 5
 
0.1%
103 3
 
0.1%
105 4
 
0.1%
106 10
 
0.2%
ValueCountFrequency (%)
190 2
 
< 0.1%
188 1
 
< 0.1%
184.4 1
 
< 0.1%
182 1
 
< 0.1%
180 9
0.2%
178 2
 
< 0.1%
177 2
 
< 0.1%
176 1
 
< 0.1%
175 1
 
< 0.1%
174 1
 
< 0.1%

Nacionality
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8731917
Minimum1
Maximum109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2023-11-04T09:31:58.121851image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum109
Range108
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.914514
Coefficient of variation (CV)3.6913008
Kurtosis135.14621
Mean1.8731917
Median Absolute Deviation (MAD)0
Skewness10.703998
Sum8287
Variance47.810504
MonotonicityNot monotonic
2023-11-04T09:31:58.220915image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 4314
97.5%
41 38
 
0.9%
26 14
 
0.3%
22 13
 
0.3%
6 13
 
0.3%
24 5
 
0.1%
100 3
 
0.1%
11 3
 
0.1%
103 3
 
0.1%
21 2
 
< 0.1%
Other values (11) 16
 
0.4%
ValueCountFrequency (%)
1 4314
97.5%
2 2
 
< 0.1%
6 13
 
0.3%
11 3
 
0.1%
13 1
 
< 0.1%
14 1
 
< 0.1%
17 1
 
< 0.1%
21 2
 
< 0.1%
22 13
 
0.3%
24 5
 
0.1%
ValueCountFrequency (%)
109 1
 
< 0.1%
108 1
 
< 0.1%
105 2
 
< 0.1%
103 3
 
0.1%
101 2
 
< 0.1%
100 3
 
0.1%
62 2
 
< 0.1%
41 38
0.9%
32 1
 
< 0.1%
26 14
 
0.3%

Mother's qualification
Real number (ℝ)

Distinct29
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.561935
Minimum1
Maximum44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2023-11-04T09:31:58.325976image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median19
Q337
95-th percentile38
Maximum44
Range43
Interquartile range (IQR)35

Descriptive statistics

Standard deviation15.603186
Coefficient of variation (CV)0.79763001
Kurtosis-1.6922924
Mean19.561935
Median Absolute Deviation (MAD)18
Skewness0.0019784779
Sum86542
Variance243.45942
MonotonicityNot monotonic
2023-11-04T09:31:58.429640image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1 1069
24.2%
37 1009
22.8%
19 953
21.5%
38 562
12.7%
3 438
9.9%
34 130
 
2.9%
2 83
 
1.9%
4 49
 
1.1%
12 42
 
0.9%
5 21
 
0.5%
Other values (19) 68
 
1.5%
ValueCountFrequency (%)
1 1069
24.2%
2 83
 
1.9%
3 438
9.9%
4 49
 
1.1%
5 21
 
0.5%
6 4
 
0.1%
9 8
 
0.2%
10 3
 
0.1%
11 3
 
0.1%
12 42
 
0.9%
ValueCountFrequency (%)
44 1
 
< 0.1%
43 4
 
0.1%
42 4
 
0.1%
41 6
 
0.1%
40 9
 
0.2%
39 8
 
0.2%
38 562
12.7%
37 1009
22.8%
36 3
 
0.1%
35 3
 
0.1%

Father's qualification
Real number (ℝ)

Distinct34
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.275316
Minimum1
Maximum44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2023-11-04T09:31:58.530421image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median19
Q337
95-th percentile38
Maximum44
Range43
Interquartile range (IQR)34

Descriptive statistics

Standard deviation15.343108
Coefficient of variation (CV)0.68879416
Kurtosis-1.5805918
Mean22.275316
Median Absolute Deviation (MAD)18
Skewness-0.29869722
Sum98546
Variance235.41096
MonotonicityNot monotonic
2023-11-04T09:31:58.716765image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
37 1209
27.3%
19 968
21.9%
1 904
20.4%
38 702
15.9%
3 282
 
6.4%
34 112
 
2.5%
2 68
 
1.5%
4 39
 
0.9%
12 38
 
0.9%
39 20
 
0.5%
Other values (24) 82
 
1.9%
ValueCountFrequency (%)
1 904
20.4%
2 68
 
1.5%
3 282
 
6.4%
4 39
 
0.9%
5 18
 
0.4%
6 2
 
< 0.1%
9 5
 
0.1%
10 2
 
< 0.1%
11 10
 
0.2%
12 38
 
0.9%
ValueCountFrequency (%)
44 1
 
< 0.1%
43 2
 
< 0.1%
42 1
 
< 0.1%
41 2
 
< 0.1%
40 5
 
0.1%
39 20
 
0.5%
38 702
15.9%
37 1209
27.3%
36 8
 
0.2%
35 2
 
< 0.1%

Mother's occupation
Real number (ℝ)

ZEROS 

Distinct32
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.960895
Minimum0
Maximum194
Zeros144
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2023-11-04T09:31:58.918491image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median5
Q39
95-th percentile9
Maximum194
Range194
Interquartile range (IQR)5

Descriptive statistics

Standard deviation26.418253
Coefficient of variation (CV)2.4102277
Kurtosis29.226145
Mean10.960895
Median Absolute Deviation (MAD)3
Skewness5.3392271
Sum48491
Variance697.92409
MonotonicityNot monotonic
2023-11-04T09:31:59.128091image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
9 1577
35.6%
4 817
18.5%
5 530
 
12.0%
3 351
 
7.9%
2 318
 
7.2%
7 272
 
6.1%
0 144
 
3.3%
1 102
 
2.3%
6 91
 
2.1%
90 70
 
1.6%
Other values (22) 152
 
3.4%
ValueCountFrequency (%)
0 144
 
3.3%
1 102
 
2.3%
2 318
 
7.2%
3 351
 
7.9%
4 817
18.5%
5 530
 
12.0%
6 91
 
2.1%
7 272
 
6.1%
8 36
 
0.8%
9 1577
35.6%
ValueCountFrequency (%)
194 11
0.2%
193 4
 
0.1%
192 5
 
0.1%
191 26
0.6%
175 5
 
0.1%
173 1
 
< 0.1%
171 1
 
< 0.1%
153 2
 
< 0.1%
152 2
 
< 0.1%
151 3
 
0.1%

Father's occupation
Real number (ℝ)

ZEROS 

Distinct46
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.032324
Minimum0
Maximum195
Zeros128
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2023-11-04T09:31:59.345778image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median7
Q39
95-th percentile10
Maximum195
Range195
Interquartile range (IQR)5

Descriptive statistics

Standard deviation25.26304
Coefficient of variation (CV)2.2899111
Kurtosis29.927395
Mean11.032324
Median Absolute Deviation (MAD)2
Skewness5.3951732
Sum48807
Variance638.2212
MonotonicityNot monotonic
2023-11-04T09:31:59.598424image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
9 1010
22.8%
7 666
15.1%
5 516
11.7%
4 386
 
8.7%
3 384
 
8.7%
8 318
 
7.2%
10 266
 
6.0%
6 242
 
5.5%
2 197
 
4.5%
1 134
 
3.0%
Other values (36) 305
 
6.9%
ValueCountFrequency (%)
0 128
 
2.9%
1 134
 
3.0%
2 197
 
4.5%
3 384
 
8.7%
4 386
 
8.7%
5 516
11.7%
6 242
 
5.5%
7 666
15.1%
8 318
 
7.2%
9 1010
22.8%
ValueCountFrequency (%)
195 1
 
< 0.1%
194 2
 
< 0.1%
193 15
0.3%
192 6
 
0.1%
183 3
 
0.1%
182 2
 
< 0.1%
181 3
 
0.1%
175 4
 
0.1%
174 1
 
< 0.1%
172 2
 
< 0.1%

Admission grade
Real number (ℝ)

HIGH CORRELATION 

Distinct620
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.97812
Minimum95
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2023-11-04T09:31:59.830726image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum95
5-th percentile103.415
Q1117.9
median126.1
Q3134.8
95-th percentile153.5
Maximum190
Range95
Interquartile range (IQR)16.9

Descriptive statistics

Standard deviation14.482001
Coefficient of variation (CV)0.11405115
Kurtosis0.6627246
Mean126.97812
Median Absolute Deviation (MAD)8.4
Skewness0.53059986
Sum561751.2
Variance209.72835
MonotonicityNot monotonic
2023-11-04T09:32:00.047447image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
130 162
 
3.7%
140 153
 
3.5%
120 145
 
3.3%
100 116
 
2.6%
150 81
 
1.8%
110 77
 
1.7%
160 43
 
1.0%
128.2 39
 
0.9%
123 26
 
0.6%
128 26
 
0.6%
Other values (610) 3556
80.4%
ValueCountFrequency (%)
95 11
0.2%
95.1 1
 
< 0.1%
95.5 2
 
< 0.1%
95.8 1
 
< 0.1%
96 7
0.2%
96.1 1
 
< 0.1%
96.7 1
 
< 0.1%
97 6
0.1%
97.2 1
 
< 0.1%
97.4 1
 
< 0.1%
ValueCountFrequency (%)
190 3
0.1%
184.4 1
 
< 0.1%
184 1
 
< 0.1%
183.5 1
 
< 0.1%
180.4 1
 
< 0.1%
180 4
0.1%
179.6 1
 
< 0.1%
178.3 1
 
< 0.1%
178 1
 
< 0.1%
176.7 1
 
< 0.1%

Displaced
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.7 KiB
1
2426 
0
1998 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4424
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 2426
54.8%
0 1998
45.2%

Length

2023-11-04T09:32:00.244991image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-04T09:32:00.397739image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1 2426
54.8%
0 1998
45.2%

Most occurring characters

ValueCountFrequency (%)
1 2426
54.8%
0 1998
45.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4424
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2426
54.8%
0 1998
45.2%

Most occurring scripts

ValueCountFrequency (%)
Common 4424
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2426
54.8%
0 1998
45.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2426
54.8%
0 1998
45.2%

Educational special needs
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.7 KiB
0
4373 
1
 
51

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4424
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4373
98.8%
1 51
 
1.2%

Length

2023-11-04T09:32:00.562960image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-04T09:32:00.662707image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 4373
98.8%
1 51
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 4373
98.8%
1 51
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4424
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4373
98.8%
1 51
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Common 4424
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4373
98.8%
1 51
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4373
98.8%
1 51
 
1.2%

Debtor
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.7 KiB
0
3921 
1
503 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4424
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3921
88.6%
1 503
 
11.4%

Length

2023-11-04T09:32:00.761625image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-04T09:32:00.851392image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 3921
88.6%
1 503
 
11.4%

Most occurring characters

ValueCountFrequency (%)
0 3921
88.6%
1 503
 
11.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4424
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3921
88.6%
1 503
 
11.4%

Most occurring scripts

ValueCountFrequency (%)
Common 4424
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3921
88.6%
1 503
 
11.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3921
88.6%
1 503
 
11.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.7 KiB
1
3896 
0
528 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4424
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 3896
88.1%
0 528
 
11.9%

Length

2023-11-04T09:32:00.944961image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-04T09:32:01.664731image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1 3896
88.1%
0 528
 
11.9%

Most occurring characters

ValueCountFrequency (%)
1 3896
88.1%
0 528
 
11.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4424
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3896
88.1%
0 528
 
11.9%

Most occurring scripts

ValueCountFrequency (%)
Common 4424
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3896
88.1%
0 528
 
11.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3896
88.1%
0 528
 
11.9%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.7 KiB
0
2868 
1
1556 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4424
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2868
64.8%
1 1556
35.2%

Length

2023-11-04T09:32:01.765568image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-04T09:32:01.854475image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 2868
64.8%
1 1556
35.2%

Most occurring characters

ValueCountFrequency (%)
0 2868
64.8%
1 1556
35.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4424
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2868
64.8%
1 1556
35.2%

Most occurring scripts

ValueCountFrequency (%)
Common 4424
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2868
64.8%
1 1556
35.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2868
64.8%
1 1556
35.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.7 KiB
0
3325 
1
1099 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4424
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3325
75.2%
1 1099
 
24.8%

Length

2023-11-04T09:32:01.946292image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-04T09:32:02.042495image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 3325
75.2%
1 1099
 
24.8%

Most occurring characters

ValueCountFrequency (%)
0 3325
75.2%
1 1099
 
24.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4424
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3325
75.2%
1 1099
 
24.8%

Most occurring scripts

ValueCountFrequency (%)
Common 4424
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3325
75.2%
1 1099
 
24.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3325
75.2%
1 1099
 
24.8%

Age at enrollment
Real number (ℝ)

HIGH CORRELATION 

Distinct46
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.265145
Minimum17
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2023-11-04T09:32:02.143604image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile18
Q119
median20
Q325
95-th percentile41
Maximum70
Range53
Interquartile range (IQR)6

Descriptive statistics

Standard deviation7.5878156
Coefficient of variation (CV)0.32614522
Kurtosis4.1268918
Mean23.265145
Median Absolute Deviation (MAD)2
Skewness2.0549884
Sum102925
Variance57.574946
MonotonicityNot monotonic
2023-11-04T09:32:02.258736image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
18 1036
23.4%
19 911
20.6%
20 599
13.5%
21 322
 
7.3%
22 174
 
3.9%
24 131
 
3.0%
23 108
 
2.4%
26 94
 
2.1%
25 93
 
2.1%
27 91
 
2.1%
Other values (36) 865
19.6%
ValueCountFrequency (%)
17 5
 
0.1%
18 1036
23.4%
19 911
20.6%
20 599
13.5%
21 322
 
7.3%
22 174
 
3.9%
23 108
 
2.4%
24 131
 
3.0%
25 93
 
2.1%
26 94
 
2.1%
ValueCountFrequency (%)
70 1
 
< 0.1%
62 1
 
< 0.1%
61 1
 
< 0.1%
60 2
 
< 0.1%
59 3
0.1%
58 3
0.1%
57 2
 
< 0.1%
55 5
0.1%
54 7
0.2%
53 7
0.2%

International
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.7 KiB
0
4314 
1
 
110

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4424
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4314
97.5%
1 110
 
2.5%

Length

2023-11-04T09:32:02.377940image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-04T09:32:02.471545image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 4314
97.5%
1 110
 
2.5%

Most occurring characters

ValueCountFrequency (%)
0 4314
97.5%
1 110
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4424
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4314
97.5%
1 110
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
Common 4424
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4314
97.5%
1 110
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4314
97.5%
1 110
 
2.5%

Curricular units 1st sem (credited)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.70999096
Minimum0
Maximum20
Zeros3847
Zeros (%)87.0%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2023-11-04T09:32:02.546444image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum20
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.3605066
Coefficient of variation (CV)3.3246995
Kurtosis19.205727
Mean0.70999096
Median Absolute Deviation (MAD)0
Skewness4.1690488
Sum3141
Variance5.5719915
MonotonicityNot monotonic
2023-11-04T09:32:02.661059image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 3847
87.0%
2 94
 
2.1%
1 85
 
1.9%
3 69
 
1.6%
6 51
 
1.2%
4 47
 
1.1%
7 41
 
0.9%
5 41
 
0.9%
8 31
 
0.7%
9 27
 
0.6%
Other values (11) 91
 
2.1%
ValueCountFrequency (%)
0 3847
87.0%
1 85
 
1.9%
2 94
 
2.1%
3 69
 
1.6%
4 47
 
1.1%
5 41
 
0.9%
6 51
 
1.2%
7 41
 
0.9%
8 31
 
0.7%
9 27
 
0.6%
ValueCountFrequency (%)
20 2
 
< 0.1%
19 2
 
< 0.1%
18 4
 
0.1%
17 3
 
0.1%
16 3
 
0.1%
15 5
 
0.1%
14 15
0.3%
13 13
0.3%
12 12
0.3%
11 17
0.4%

Curricular units 1st sem (enrolled)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2705696
Minimum0
Maximum26
Zeros180
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2023-11-04T09:32:02.778779image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q15
median6
Q37
95-th percentile11
Maximum26
Range26
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.4801782
Coefficient of variation (CV)0.39552677
Kurtosis8.9379154
Mean6.2705696
Median Absolute Deviation (MAD)1
Skewness1.6190409
Sum27741
Variance6.1512838
MonotonicityNot monotonic
2023-11-04T09:32:02.893363image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
6 1910
43.2%
5 1010
22.8%
7 656
 
14.8%
8 296
 
6.7%
0 180
 
4.1%
12 66
 
1.5%
10 52
 
1.2%
11 45
 
1.0%
9 36
 
0.8%
15 25
 
0.6%
Other values (13) 148
 
3.3%
ValueCountFrequency (%)
0 180
 
4.1%
1 7
 
0.2%
2 9
 
0.2%
3 10
 
0.2%
4 21
 
0.5%
5 1010
22.8%
6 1910
43.2%
7 656
 
14.8%
8 296
 
6.7%
9 36
 
0.8%
ValueCountFrequency (%)
26 1
 
< 0.1%
23 2
 
< 0.1%
21 6
 
0.1%
19 2
 
< 0.1%
18 19
0.4%
17 16
0.4%
16 13
0.3%
15 25
0.6%
14 22
0.5%
13 20
0.5%

Curricular units 1st sem (evaluations)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct35
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.2990506
Minimum0
Maximum45
Zeros349
Zeros (%)7.9%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2023-11-04T09:32:03.010250image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median8
Q310
95-th percentile15
Maximum45
Range45
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.1791056
Coefficient of variation (CV)0.50356429
Kurtosis5.4630252
Mean8.2990506
Median Absolute Deviation (MAD)2
Skewness0.9766367
Sum36715
Variance17.464923
MonotonicityNot monotonic
2023-11-04T09:32:03.112773image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
8 791
17.9%
7 703
15.9%
6 598
13.5%
9 402
9.1%
0 349
7.9%
10 340
7.7%
11 239
 
5.4%
12 223
 
5.0%
5 220
 
5.0%
13 140
 
3.2%
Other values (25) 419
9.5%
ValueCountFrequency (%)
0 349
7.9%
1 6
 
0.1%
2 8
 
0.2%
3 6
 
0.1%
4 19
 
0.4%
5 220
 
5.0%
6 598
13.5%
7 703
15.9%
8 791
17.9%
9 402
9.1%
ValueCountFrequency (%)
45 2
< 0.1%
36 1
 
< 0.1%
33 1
 
< 0.1%
32 1
 
< 0.1%
31 1
 
< 0.1%
29 2
< 0.1%
28 1
 
< 0.1%
27 2
< 0.1%
26 4
0.1%
25 3
0.1%

Curricular units 1st sem (approved)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7066004
Minimum0
Maximum26
Zeros718
Zeros (%)16.2%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2023-11-04T09:32:03.220698image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median5
Q36
95-th percentile9
Maximum26
Range26
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.094238
Coefficient of variation (CV)0.65742526
Kurtosis3.0966799
Mean4.7066004
Median Absolute Deviation (MAD)1
Skewness0.7662624
Sum20822
Variance9.5743087
MonotonicityNot monotonic
2023-11-04T09:32:03.310612image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
6 1171
26.5%
5 723
16.3%
0 718
16.2%
7 471
10.6%
4 433
 
9.8%
3 269
 
6.1%
2 160
 
3.6%
1 127
 
2.9%
8 108
 
2.4%
11 49
 
1.1%
Other values (13) 195
 
4.4%
ValueCountFrequency (%)
0 718
16.2%
1 127
 
2.9%
2 160
 
3.6%
3 269
 
6.1%
4 433
 
9.8%
5 723
16.3%
6 1171
26.5%
7 471
10.6%
8 108
 
2.4%
9 40
 
0.9%
ValueCountFrequency (%)
26 1
 
< 0.1%
21 4
 
0.1%
20 3
 
0.1%
19 2
 
< 0.1%
18 15
0.3%
17 10
 
0.2%
16 5
 
0.1%
15 7
 
0.2%
14 14
0.3%
13 26
0.6%

Curricular units 1st sem (grade)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct797
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.640822
Minimum0
Maximum18.875
Zeros718
Zeros (%)16.2%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2023-11-04T09:32:03.429446image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111
median12.285714
Q313.4
95-th percentile14.857143
Maximum18.875
Range18.875
Interquartile range (IQR)2.4

Descriptive statistics

Standard deviation4.8436634
Coefficient of variation (CV)0.45519637
Kurtosis0.90846103
Mean10.640822
Median Absolute Deviation (MAD)1.1571428
Skewness-1.5681456
Sum47074.995
Variance23.461075
MonotonicityNot monotonic
2023-11-04T09:32:03.563779image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 718
 
16.2%
12 205
 
4.6%
13 147
 
3.3%
11 138
 
3.1%
11.5 89
 
2.0%
14 85
 
1.9%
12.5 84
 
1.9%
10 82
 
1.9%
12.66666667 82
 
1.9%
12.33333333 82
 
1.9%
Other values (787) 2712
61.3%
ValueCountFrequency (%)
0 718
16.2%
9.8 1
 
< 0.1%
10 82
 
1.9%
10.16666667 1
 
< 0.1%
10.2 8
 
0.2%
10.21428571 1
 
< 0.1%
10.25 7
 
0.2%
10.28571429 1
 
< 0.1%
10.33333333 16
 
0.4%
10.36842105 1
 
< 0.1%
ValueCountFrequency (%)
18.875 1
 
< 0.1%
18 2
 
< 0.1%
17.33333333 2
 
< 0.1%
17.125 1
 
< 0.1%
17.11111111 1
 
< 0.1%
17.00555556 1
 
< 0.1%
17 5
0.1%
16.9 1
 
< 0.1%
16.88571429 1
 
< 0.1%
16.85714286 1
 
< 0.1%
Distinct11
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13765823
Minimum0
Maximum12
Zeros4130
Zeros (%)93.4%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2023-11-04T09:32:03.664661image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum12
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.69088018
Coefficient of variation (CV)5.0188078
Kurtosis89.863208
Mean0.13765823
Median Absolute Deviation (MAD)0
Skewness8.2074031
Sum609
Variance0.47731543
MonotonicityNot monotonic
2023-11-04T09:32:03.764545image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 4130
93.4%
1 153
 
3.5%
2 79
 
1.8%
3 23
 
0.5%
4 15
 
0.3%
6 6
 
0.1%
7 6
 
0.1%
5 5
 
0.1%
8 4
 
0.1%
12 2
 
< 0.1%
ValueCountFrequency (%)
0 4130
93.4%
1 153
 
3.5%
2 79
 
1.8%
3 23
 
0.5%
4 15
 
0.3%
5 5
 
0.1%
6 6
 
0.1%
7 6
 
0.1%
8 4
 
0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
12 2
 
< 0.1%
10 1
 
< 0.1%
8 4
 
0.1%
7 6
 
0.1%
6 6
 
0.1%
5 5
 
0.1%
4 15
 
0.3%
3 23
 
0.5%
2 79
1.8%
1 153
3.5%

Curricular units 2nd sem (credited)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.54181736
Minimum0
Maximum19
Zeros3894
Zeros (%)88.0%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2023-11-04T09:32:03.862683image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum19
Range19
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.9185461
Coefficient of variation (CV)3.5409462
Kurtosis24.427266
Mean0.54181736
Median Absolute Deviation (MAD)0
Skewness4.6348195
Sum2397
Variance3.6808193
MonotonicityNot monotonic
2023-11-04T09:32:03.978315image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 3894
88.0%
1 107
 
2.4%
2 92
 
2.1%
4 78
 
1.8%
5 68
 
1.5%
3 49
 
1.1%
6 26
 
0.6%
11 20
 
0.5%
7 16
 
0.4%
9 15
 
0.3%
Other values (9) 59
 
1.3%
ValueCountFrequency (%)
0 3894
88.0%
1 107
 
2.4%
2 92
 
2.1%
3 49
 
1.1%
4 78
 
1.8%
5 68
 
1.5%
6 26
 
0.6%
7 16
 
0.4%
8 12
 
0.3%
9 15
 
0.3%
ValueCountFrequency (%)
19 1
 
< 0.1%
18 2
 
< 0.1%
16 2
 
< 0.1%
15 2
 
< 0.1%
14 4
 
0.1%
13 9
0.2%
12 14
0.3%
11 20
0.5%
10 13
0.3%
9 15
0.3%

Curricular units 2nd sem (enrolled)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2321429
Minimum0
Maximum23
Zeros180
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2023-11-04T09:32:04.083397image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q15
median6
Q37
95-th percentile10
Maximum23
Range23
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.1959508
Coefficient of variation (CV)0.35235886
Kurtosis7.13474
Mean6.2321429
Median Absolute Deviation (MAD)1
Skewness0.7881135
Sum27571
Variance4.8221997
MonotonicityNot monotonic
2023-11-04T09:32:04.183611image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
6 1913
43.2%
5 1054
23.8%
8 661
 
14.9%
7 304
 
6.9%
0 180
 
4.1%
11 60
 
1.4%
9 50
 
1.1%
10 48
 
1.1%
12 44
 
1.0%
13 37
 
0.8%
Other values (12) 73
 
1.7%
ValueCountFrequency (%)
0 180
 
4.1%
1 3
 
0.1%
2 5
 
0.1%
3 3
 
0.1%
4 17
 
0.4%
5 1054
23.8%
6 1913
43.2%
7 304
 
6.9%
8 661
 
14.9%
9 50
 
1.1%
ValueCountFrequency (%)
23 2
 
< 0.1%
21 1
 
< 0.1%
19 3
 
0.1%
18 2
 
< 0.1%
17 12
 
0.3%
16 1
 
< 0.1%
15 2
 
< 0.1%
14 22
0.5%
13 37
0.8%
12 44
1.0%

Curricular units 2nd sem (evaluations)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.0632911
Minimum0
Maximum33
Zeros401
Zeros (%)9.1%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2023-11-04T09:32:04.283845image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median8
Q310
95-th percentile15
Maximum33
Range33
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.9479509
Coefficient of variation (CV)0.48962029
Kurtosis2.0682859
Mean8.0632911
Median Absolute Deviation (MAD)2
Skewness0.33649718
Sum35672
Variance15.586317
MonotonicityNot monotonic
2023-11-04T09:32:04.384260image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
8 792
17.9%
6 614
13.9%
7 563
12.7%
9 456
10.3%
0 401
9.1%
10 355
8.0%
5 288
 
6.5%
11 255
 
5.8%
12 226
 
5.1%
13 126
 
2.8%
Other values (20) 348
7.9%
ValueCountFrequency (%)
0 401
9.1%
1 3
 
0.1%
2 4
 
0.1%
3 2
 
< 0.1%
4 10
 
0.2%
5 288
 
6.5%
6 614
13.9%
7 563
12.7%
8 792
17.9%
9 456
10.3%
ValueCountFrequency (%)
33 1
 
< 0.1%
28 1
 
< 0.1%
27 2
 
< 0.1%
26 3
 
0.1%
25 1
 
< 0.1%
24 3
 
0.1%
23 4
 
0.1%
22 10
0.2%
21 10
0.2%
20 8
0.2%

Curricular units 2nd sem (approved)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4358047
Minimum0
Maximum20
Zeros870
Zeros (%)19.7%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2023-11-04T09:32:04.494904image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q36
95-th percentile8
Maximum20
Range20
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.0147639
Coefficient of variation (CV)0.67964306
Kurtosis0.84504466
Mean4.4358047
Median Absolute Deviation (MAD)2
Skewness0.30627938
Sum19624
Variance9.0888014
MonotonicityNot monotonic
2023-11-04T09:32:04.613790image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
6 965
21.8%
0 870
19.7%
5 726
16.4%
4 414
9.4%
7 331
 
7.5%
8 321
 
7.3%
3 285
 
6.4%
2 198
 
4.5%
1 114
 
2.6%
11 48
 
1.1%
Other values (10) 152
 
3.4%
ValueCountFrequency (%)
0 870
19.7%
1 114
 
2.6%
2 198
 
4.5%
3 285
 
6.4%
4 414
9.4%
5 726
16.4%
6 965
21.8%
7 331
 
7.5%
8 321
 
7.3%
9 36
 
0.8%
ValueCountFrequency (%)
20 2
 
< 0.1%
19 3
 
0.1%
18 2
 
< 0.1%
17 8
 
0.2%
16 2
 
< 0.1%
14 6
 
0.1%
13 21
0.5%
12 34
0.8%
11 48
1.1%
10 38
0.9%

Curricular units 2nd sem (grade)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct782
Distinct (%)17.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.230206
Minimum0
Maximum18.571429
Zeros870
Zeros (%)19.7%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2023-11-04T09:32:04.729650image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110.75
median12.2
Q313.333333
95-th percentile14.980262
Maximum18.571429
Range18.571429
Interquartile range (IQR)2.5833333

Descriptive statistics

Standard deviation5.210808
Coefficient of variation (CV)0.50935515
Kurtosis0.066567351
Mean10.230206
Median Absolute Deviation (MAD)1.2
Skewness-1.3136502
Sum45258.43
Variance27.15252
MonotonicityNot monotonic
2023-11-04T09:32:04.863435image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 870
 
19.7%
12 170
 
3.8%
11 165
 
3.7%
13 150
 
3.4%
11.5 86
 
1.9%
12.5 84
 
1.9%
14 77
 
1.7%
10 77
 
1.7%
13.5 65
 
1.5%
12.66666667 61
 
1.4%
Other values (772) 2619
59.2%
ValueCountFrequency (%)
0 870
19.7%
10 77
 
1.7%
10.16666667 4
 
0.1%
10.2 4
 
0.1%
10.25 10
 
0.2%
10.33333333 19
 
0.4%
10.375 1
 
< 0.1%
10.4 8
 
0.2%
10.42857143 2
 
< 0.1%
10.44444444 2
 
< 0.1%
ValueCountFrequency (%)
18.57142857 1
< 0.1%
17.71428571 1
< 0.1%
17.69230769 1
< 0.1%
17.6 2
< 0.1%
17.5875 1
< 0.1%
17.42857143 1
< 0.1%
17.16666667 1
< 0.1%
17 2
< 0.1%
16.90909091 1
< 0.1%
16.8 2
< 0.1%
Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15031646
Minimum0
Maximum12
Zeros4142
Zeros (%)93.6%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2023-11-04T09:32:04.984012image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum12
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.75377407
Coefficient of variation (CV)5.0145812
Kurtosis66.811692
Mean0.15031646
Median Absolute Deviation (MAD)0
Skewness7.2677009
Sum665
Variance0.56817535
MonotonicityNot monotonic
2023-11-04T09:32:05.092965image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 4142
93.6%
1 140
 
3.2%
2 48
 
1.1%
3 35
 
0.8%
4 21
 
0.5%
5 17
 
0.4%
6 8
 
0.2%
8 6
 
0.1%
7 5
 
0.1%
12 2
 
< 0.1%
ValueCountFrequency (%)
0 4142
93.6%
1 140
 
3.2%
2 48
 
1.1%
3 35
 
0.8%
4 21
 
0.5%
5 17
 
0.4%
6 8
 
0.2%
7 5
 
0.1%
8 6
 
0.1%
12 2
 
< 0.1%
ValueCountFrequency (%)
12 2
 
< 0.1%
8 6
 
0.1%
7 5
 
0.1%
6 8
 
0.2%
5 17
 
0.4%
4 21
 
0.5%
3 35
 
0.8%
2 48
 
1.1%
1 140
 
3.2%
0 4142
93.6%

Unemployment rate
Real number (ℝ)

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.566139
Minimum7.6
Maximum16.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2023-11-04T09:32:05.185545image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum7.6
5-th percentile7.6
Q19.4
median11.1
Q313.9
95-th percentile16.2
Maximum16.2
Range8.6
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation2.6638505
Coefficient of variation (CV)0.23031458
Kurtosis-0.99552591
Mean11.566139
Median Absolute Deviation (MAD)1.7
Skewness0.21205105
Sum51168.6
Variance7.0960994
MonotonicityNot monotonic
2023-11-04T09:32:05.277569image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
7.6 571
12.9%
9.4 533
12.0%
10.8 525
11.9%
12.4 445
10.1%
12.7 419
9.5%
11.1 414
9.4%
15.5 397
9.0%
13.9 390
8.8%
8.9 368
8.3%
16.2 362
8.2%
ValueCountFrequency (%)
7.6 571
12.9%
8.9 368
8.3%
9.4 533
12.0%
10.8 525
11.9%
11.1 414
9.4%
12.4 445
10.1%
12.7 419
9.5%
13.9 390
8.8%
15.5 397
9.0%
16.2 362
8.2%
ValueCountFrequency (%)
16.2 362
8.2%
15.5 397
9.0%
13.9 390
8.8%
12.7 419
9.5%
12.4 445
10.1%
11.1 414
9.4%
10.8 525
11.9%
9.4 533
12.0%
8.9 368
8.3%
7.6 571
12.9%

Inflation rate
Real number (ℝ)

Distinct9
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2280289
Minimum-0.8
Maximum3.7
Zeros0
Zeros (%)0.0%
Negative923
Negative (%)20.9%
Memory size34.7 KiB
2023-11-04T09:32:05.362157image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-0.8
5-th percentile-0.8
Q10.3
median1.4
Q32.6
95-th percentile3.7
Maximum3.7
Range4.5
Interquartile range (IQR)2.3

Descriptive statistics

Standard deviation1.3827107
Coefficient of variation (CV)1.1259594
Kurtosis-1.0390334
Mean1.2280289
Median Absolute Deviation (MAD)1.2
Skewness0.25237535
Sum5432.8
Variance1.9118889
MonotonicityNot monotonic
2023-11-04T09:32:05.446243image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1.4 893
20.2%
2.6 571
12.9%
-0.8 533
12.0%
0.5 445
10.1%
3.7 419
9.5%
0.6 414
9.4%
2.8 397
9.0%
-0.3 390
8.8%
0.3 362
8.2%
ValueCountFrequency (%)
-0.8 533
12.0%
-0.3 390
8.8%
0.3 362
8.2%
0.5 445
10.1%
0.6 414
9.4%
1.4 893
20.2%
2.6 571
12.9%
2.8 397
9.0%
3.7 419
9.5%
ValueCountFrequency (%)
3.7 419
9.5%
2.8 397
9.0%
2.6 571
12.9%
1.4 893
20.2%
0.6 414
9.4%
0.5 445
10.1%
0.3 362
8.2%
-0.3 390
8.8%
-0.8 533
12.0%

GDP
Real number (ℝ)

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0019688065
Minimum-4.06
Maximum3.51
Zeros0
Zeros (%)0.0%
Negative1711
Negative (%)38.7%
Memory size34.7 KiB
2023-11-04T09:32:05.540454image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-4.06
5-th percentile-4.06
Q1-1.7
median0.32
Q31.79
95-th percentile3.51
Maximum3.51
Range7.57
Interquartile range (IQR)3.49

Descriptive statistics

Standard deviation2.2699354
Coefficient of variation (CV)1152.95
Kurtosis-1.0016532
Mean0.0019688065
Median Absolute Deviation (MAD)1.47
Skewness-0.39406821
Sum8.71
Variance5.1526069
MonotonicityNot monotonic
2023-11-04T09:32:05.627301image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.32 571
12.9%
-3.12 533
12.0%
1.74 525
11.9%
1.79 445
10.1%
-1.7 419
9.5%
2.02 414
9.4%
-4.06 397
9.0%
0.79 390
8.8%
3.51 368
8.3%
-0.92 362
8.2%
ValueCountFrequency (%)
-4.06 397
9.0%
-3.12 533
12.0%
-1.7 419
9.5%
-0.92 362
8.2%
0.32 571
12.9%
0.79 390
8.8%
1.74 525
11.9%
1.79 445
10.1%
2.02 414
9.4%
3.51 368
8.3%
ValueCountFrequency (%)
3.51 368
8.3%
2.02 414
9.4%
1.79 445
10.1%
1.74 525
11.9%
0.79 390
8.8%
0.32 571
12.9%
-0.92 362
8.2%
-1.7 419
9.5%
-3.12 533
12.0%
-4.06 397
9.0%

Target
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.7 KiB
Graduate
2209 
Dropout
1421 
Enrolled
794 

Length

Max length8
Median length8
Mean length7.6787975
Min length7

Characters and Unicode

Total characters33971
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDropout
2nd rowGraduate
3rd rowDropout
4th rowGraduate
5th rowGraduate

Common Values

ValueCountFrequency (%)
Graduate 2209
49.9%
Dropout 1421
32.1%
Enrolled 794
 
17.9%

Length

2023-11-04T09:32:05.747986image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-04T09:32:05.844268image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
graduate 2209
49.9%
dropout 1421
32.1%
enrolled 794
 
17.9%

Most occurring characters

ValueCountFrequency (%)
r 4424
13.0%
a 4418
13.0%
o 3636
10.7%
u 3630
10.7%
t 3630
10.7%
d 3003
8.8%
e 3003
8.8%
G 2209
6.5%
l 1588
 
4.7%
D 1421
 
4.2%
Other values (3) 3009
8.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 29547
87.0%
Uppercase Letter 4424
 
13.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 4424
15.0%
a 4418
15.0%
o 3636
12.3%
u 3630
12.3%
t 3630
12.3%
d 3003
10.2%
e 3003
10.2%
l 1588
 
5.4%
p 1421
 
4.8%
n 794
 
2.7%
Uppercase Letter
ValueCountFrequency (%)
G 2209
49.9%
D 1421
32.1%
E 794
 
17.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 33971
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 4424
13.0%
a 4418
13.0%
o 3636
10.7%
u 3630
10.7%
t 3630
10.7%
d 3003
8.8%
e 3003
8.8%
G 2209
6.5%
l 1588
 
4.7%
D 1421
 
4.2%
Other values (3) 3009
8.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33971
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 4424
13.0%
a 4418
13.0%
o 3636
10.7%
u 3630
10.7%
t 3630
10.7%
d 3003
8.8%
e 3003
8.8%
G 2209
6.5%
l 1588
 
4.7%
D 1421
 
4.2%
Other values (3) 3009
8.9%

Interactions

2023-11-04T09:31:53.447848image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:30:39.715307image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:30:42.260768image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:30:44.757350image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:30:47.282433image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:30:50.816094image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:30:53.450798image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:30:55.853859image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:30:58.713029image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:01.441201image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:04.749707image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:07.998300image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:10.481919image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:12.814755image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:15.162520image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:17.989255image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:20.353706image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:23.631142image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:26.083191image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:30.098656image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:32.985065image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:35.510372image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:37.950280image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:40.480545image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:43.116588image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:46.114933image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:48.648875image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:51.030900image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:53.548108image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:30:39.815010image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:30:42.350087image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:30:44.835017image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:30:47.368100image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:30:50.951256image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:30:53.549717image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:30:55.937139image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:30:58.798929image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:01.518831image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:04.849304image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:08.086837image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:10.548255image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:12.897598image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:15.247028image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:18.065717image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:20.430978image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:23.709601image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:26.164742image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:30.184497image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:33.064711image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:35.617000image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:38.030923image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:40.576035image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:43.201913image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:46.214026image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:48.716153image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:51.110095image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:53.632554image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:30:40.005109image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:30:42.433228image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:30:44.909402image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:30:47.450289image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:30:51.060255image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:30:53.632144image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:30:56.014318image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:30:59.102652image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:01.603498image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:04.947468image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:08.178154image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:10.627012image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:12.967113image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:15.332930image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:18.145856image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:20.517962image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:23.803883image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:26.256111image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:30.267043image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:33.168699image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:35.696007image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:38.109495image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:40.670065image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:43.760843image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:46.298402image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:48.811404image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:51.196084image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:53.713254image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:30:40.080191image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:30:42.511643image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:30:44.994656image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:30:47.519040image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:30:51.131095image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:30:53.721267image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:30:56.114149image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:30:59.214286image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:01.699783image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:05.034145image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:08.265209image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:10.710773image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:13.045887image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:15.429428image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:18.230151image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:20.603062image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:23.881023image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:26.330177image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:30.369375image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:33.261339image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:35.788359image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:38.196907image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:40.763060image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:43.859600image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:46.380145image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:48.897199image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:51.277261image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:53.809915image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:30:40.158700image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:30:42.598920image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:30:45.080536image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:30:47.632144image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:30:51.214564image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:30:53.799159image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:30:56.195436image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:30:59.296967image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:01.780203image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:05.122626image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:08.363962image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:10.798851image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:13.145895image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:15.516661image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:18.312379image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:20.694779image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:23.963716image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:26.425175image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:30.497667image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:33.349664image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:35.866824image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:38.280594image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:40.847991image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:43.952044image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:46.464892image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:48.982091image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:51.348146image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:53.878232image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:30:40.235180image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:30:42.684631image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
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2023-11-04T09:31:17.812451image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:20.183089image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:23.467901image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:25.881969image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:29.895684image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:32.814283image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:35.347259image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:37.780149image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:40.315794image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:42.949422image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:45.931283image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:48.459837image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:50.865629image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:53.277545image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:55.863869image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:30:42.122277image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:30:44.668559image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:30:47.183850image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:30:50.672021image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:30:53.348871image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:30:55.771525image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:30:58.631124image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:01.313604image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:04.656456image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:07.643435image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:10.378633image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:12.728321image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:15.062888image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:17.895685image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:20.263456image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:23.536258image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:25.981519image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:29.985347image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:32.893506image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:35.433042image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:37.864163image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:40.393045image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:43.032873image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:46.025783image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:48.548747image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:50.947515image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-04T09:31:53.379195image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-11-04T09:32:05.964872image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Marital StatusApplication modeApplication orderCoursePrevious qualificationPrevious qualification (grade)NacionalityMother's qualificationFather's qualificationMother's occupationFather's occupationAdmission gradeAge at enrollmentCurricular units 1st sem (credited)Curricular units 1st sem (enrolled)Curricular units 1st sem (evaluations)Curricular units 1st sem (approved)Curricular units 1st sem (grade)Curricular units 1st sem (without evaluations)Curricular units 2nd sem (credited)Curricular units 2nd sem (enrolled)Curricular units 2nd sem (evaluations)Curricular units 2nd sem (approved)Curricular units 2nd sem (grade)Curricular units 2nd sem (without evaluations)Unemployment rateInflation rateGDPDaytime/evening attendanceDisplacedEducational special needsDebtorTuition fees up to dateGenderScholarship holderInternationalTarget
Marital Status1.0000.270-0.1740.0320.189-0.022-0.0300.1930.1220.1210.0580.0010.4830.102-0.0010.059-0.061-0.0850.0430.102-0.0200.009-0.059-0.0860.056-0.038-0.000-0.0680.3660.2750.0000.0300.0930.0440.1040.0000.078
Application mode0.2701.000-0.285-0.0870.424-0.0290.0030.0690.0460.031-0.017-0.0240.5430.3510.0660.199-0.080-0.1550.0290.3440.0460.150-0.104-0.1400.0510.091-0.026-0.0210.3950.3970.0140.1450.1950.1740.2130.0670.216
Application order-0.174-0.2851.0000.133-0.253-0.071-0.020-0.058-0.049-0.044-0.023-0.114-0.366-0.2020.056-0.0960.0900.074-0.041-0.2010.075-0.0530.1060.065-0.030-0.104-0.0110.0300.1850.3820.0000.0900.0730.1080.0890.0000.079
Course0.032-0.0870.1331.000-0.145-0.056-0.0040.0530.0360.0370.023-0.077-0.073-0.1690.1790.0010.1430.119-0.109-0.1650.1880.0180.1570.146-0.032-0.0360.026-0.0250.6470.1860.0250.0170.0000.1190.0180.0240.039
Previous qualification0.1890.424-0.253-0.1451.0000.068-0.0410.0260.015-0.010-0.0030.1380.3890.3190.0720.115-0.039-0.0330.0220.3140.0550.072-0.052-0.0440.0330.082-0.0420.0280.1480.1690.0000.1430.1230.1080.0810.0000.111
Previous qualification (grade)-0.022-0.029-0.071-0.0560.0681.0000.042-0.038-0.009-0.029-0.0330.585-0.1450.0100.035-0.0690.0930.1820.003-0.0010.036-0.0610.0820.153-0.0160.0480.019-0.0720.1250.0940.0000.0630.0790.0310.0640.0990.097
Nacionality-0.0300.003-0.020-0.004-0.0410.0421.000-0.035-0.0850.0320.0170.0170.0200.0230.0050.0060.0010.0110.0100.019-0.008-0.016-0.016-0.003-0.018-0.011-0.0030.0540.0000.0000.0000.0800.0470.0130.0100.9120.016
Mother's qualification0.1930.069-0.0580.0530.026-0.038-0.0351.0000.4340.3520.163-0.0330.172-0.0010.0170.038-0.018-0.051-0.0060.0060.0040.013-0.009-0.0410.032-0.0870.042-0.0890.2300.1050.0090.0320.0540.0740.1160.0740.125
Father's qualification0.1220.046-0.0490.0360.015-0.009-0.0850.4341.0000.2520.244-0.0230.086-0.007-0.0000.0350.002-0.023-0.0030.003-0.0030.0060.015-0.017-0.001-0.0480.040-0.0620.1940.0730.0000.0000.0410.0830.1460.0880.108
Mother's occupation0.1210.031-0.0440.037-0.010-0.0290.0320.3520.2521.0000.479-0.0240.106-0.0170.004-0.0060.000-0.026-0.014-0.0150.002-0.0130.022-0.0020.0080.0020.0410.0290.0580.0650.0120.0980.0930.0080.0530.0400.118
Father's occupation0.058-0.017-0.0230.023-0.003-0.0330.0170.1630.2440.4791.000-0.0390.038-0.0270.004-0.0330.014-0.005-0.042-0.0150.005-0.0230.0290.012-0.0540.0020.0450.0540.0550.0800.0330.1030.0530.0000.0460.0560.108
Admission grade0.001-0.024-0.114-0.0770.1380.5850.017-0.033-0.023-0.024-0.0391.000-0.1020.075-0.006-0.0950.1000.209-0.0000.0750.001-0.0610.0990.189-0.0240.027-0.018-0.0400.1040.1360.0000.0680.0680.0320.0900.1410.106
Age at enrollment0.4830.543-0.366-0.0730.389-0.1450.0200.1720.0860.1060.038-0.1021.0000.2990.0010.171-0.166-0.2100.0610.301-0.0310.083-0.188-0.2130.0940.0190.018-0.0570.4890.3890.0000.1330.2090.1620.2120.0000.219
Curricular units 1st sem (credited)0.1020.351-0.202-0.1690.3190.0100.023-0.001-0.007-0.017-0.0270.0750.2991.0000.4230.3660.3620.1010.1000.9140.3760.3310.2890.0930.0640.024-0.0020.0220.1710.1070.0000.0570.0000.0270.0860.0000.045
Curricular units 1st sem (enrolled)-0.0010.0660.0560.1790.0720.0350.0050.017-0.0000.0040.004-0.0060.0010.4231.0000.4200.7070.363-0.0190.4400.9620.4310.6530.351-0.0210.1080.0170.0180.2190.1500.0430.0470.0830.2130.1560.0000.173
Curricular units 1st sem (evaluations)0.0590.199-0.0960.0010.115-0.0690.0060.0380.035-0.006-0.033-0.0950.1710.3660.4201.0000.2620.1120.2020.3690.3860.6940.2520.0900.1530.067-0.040-0.0970.0630.0980.0000.0610.1030.0790.1870.0560.258
Curricular units 1st sem (approved)-0.061-0.0800.0900.143-0.0390.0930.001-0.0180.0020.0000.0140.100-0.1660.3620.7070.2621.0000.640-0.0690.3670.6990.3640.8920.663-0.0730.074-0.0020.0590.1730.1290.0000.1450.2800.2370.2640.0000.455
Curricular units 1st sem (grade)-0.085-0.1550.0740.119-0.0330.1820.011-0.051-0.023-0.026-0.0050.209-0.2100.1010.3630.1120.6401.000-0.0170.0970.3680.1820.6290.762-0.0470.045-0.0370.0920.1380.0870.0000.1060.2480.1930.1810.0000.383
Curricular units 1st sem (without evaluations)0.0430.029-0.041-0.1090.0220.0030.010-0.006-0.003-0.014-0.042-0.0000.0610.100-0.0190.202-0.069-0.0171.0000.059-0.0320.096-0.055-0.0410.384-0.067-0.068-0.1840.0190.0000.0000.0310.0780.0000.0570.0570.062
Curricular units 2nd sem (credited)0.1020.344-0.201-0.1650.314-0.0010.0190.0060.003-0.015-0.0150.0750.3010.9140.4400.3690.3670.0970.0591.0000.4140.3420.3190.1030.0820.012-0.0010.0240.1820.1280.0000.0540.0000.0280.0750.0000.042
Curricular units 2nd sem (enrolled)-0.0200.0460.0750.1880.0550.036-0.0080.004-0.0030.0020.0050.001-0.0310.3760.9620.3860.6990.368-0.0320.4141.0000.4400.6740.364-0.0270.1390.0090.0190.1870.1380.0000.0750.1240.1620.1130.0300.139
Curricular units 2nd sem (evaluations)0.0090.150-0.0530.0180.072-0.061-0.0160.0130.006-0.013-0.023-0.0610.0830.3310.4310.6940.3640.1820.0960.3420.4401.0000.3030.1720.1590.061-0.024-0.0040.1080.0680.0290.0590.1240.1090.1660.0110.274
Curricular units 2nd sem (approved)-0.059-0.1040.1060.157-0.0520.082-0.016-0.0090.0150.0220.0290.099-0.1880.2890.6530.2520.8920.629-0.0550.3190.6740.3031.0000.694-0.0640.070-0.0150.0480.1010.1160.0000.1800.3130.2620.2730.0250.516
Curricular units 2nd sem (grade)-0.086-0.1400.0650.146-0.0440.153-0.003-0.041-0.017-0.0020.0120.189-0.2130.0930.3510.0900.6630.762-0.0410.1030.3640.1720.6941.000-0.0520.042-0.0430.1050.0850.0670.0000.1500.2960.2010.1950.0000.452
Curricular units 2nd sem (without evaluations)0.0560.051-0.030-0.0320.033-0.016-0.0180.032-0.0010.008-0.054-0.0240.0940.064-0.0210.153-0.073-0.0470.3840.082-0.0270.159-0.064-0.0521.000-0.053-0.027-0.1110.0000.0360.0000.0630.0710.0550.0400.0000.066
Unemployment rate-0.0380.091-0.104-0.0360.0820.048-0.011-0.087-0.0480.0020.0020.0270.0190.0240.1080.0670.0740.045-0.0670.0120.1390.0610.0700.042-0.0531.000-0.055-0.2880.0930.1380.0480.1350.0910.0760.1300.0690.053
Inflation rate-0.000-0.026-0.0110.026-0.0420.019-0.0030.0420.0400.0410.045-0.0180.018-0.0020.017-0.040-0.002-0.037-0.068-0.0010.009-0.024-0.015-0.043-0.027-0.0551.000-0.1020.0730.0580.0420.0860.0860.0680.0980.0300.036
GDP-0.068-0.0210.030-0.0250.028-0.0720.054-0.089-0.0620.0290.054-0.040-0.0570.0220.018-0.0970.0590.092-0.1840.0240.019-0.0040.0480.105-0.111-0.288-0.1021.0000.0930.1400.0570.1380.0790.0810.1290.0650.052
Daytime/evening attendance0.3660.3950.1850.6470.1480.1250.0000.2300.1940.0580.0550.1040.4890.1710.2190.0630.1730.1380.0190.1820.1870.1080.1010.0850.0000.0930.0730.0931.0000.2510.0230.0000.0350.0000.0920.0210.078
Displaced0.2750.3970.3820.1860.1690.0940.0000.1050.0730.0650.0800.1360.3890.1070.1500.0980.1290.0870.0000.1280.1380.0680.1160.0670.0360.1380.0580.1400.2511.0000.0000.0880.0940.1240.0710.0000.112
Educational special needs0.0000.0140.0000.0250.0000.0000.0000.0090.0000.0120.0330.0000.0000.0000.0430.0000.0000.0000.0000.0000.0000.0290.0000.0000.0000.0480.0420.0570.0230.0001.0000.0000.0000.0030.0110.0000.000
Debtor0.0300.1450.0900.0170.1430.0630.0800.0320.0000.0980.1030.0680.1330.0570.0470.0610.1450.1060.0310.0540.0750.0590.1800.1500.0630.1350.0860.1380.0000.0880.0001.0000.4070.0510.0650.0720.241
Tuition fees up to date0.0930.1950.0730.0000.1230.0790.0470.0540.0410.0930.0530.0680.2090.0000.0830.1030.2800.2480.0780.0000.1240.1240.3130.2960.0710.0910.0860.0790.0350.0940.0000.4071.0000.1020.1360.0390.431
Gender0.0440.1740.1080.1190.1080.0310.0130.0740.0830.0080.0000.0320.1620.0270.2130.0790.2370.1930.0000.0280.1620.1090.2620.2010.0550.0760.0680.0810.0000.1240.0030.0510.1021.0000.1680.0200.229
Scholarship holder0.1040.2130.0890.0180.0810.0640.0100.1160.1460.0530.0460.0900.2120.0860.1560.1870.2640.1810.0570.0750.1130.1660.2730.1950.0400.1300.0980.1290.0920.0710.0110.0650.1360.1681.0000.0220.304
International0.0000.0670.0000.0240.0000.0990.9120.0740.0880.0400.0560.1410.0000.0000.0000.0560.0000.0000.0570.0000.0300.0110.0250.0000.0000.0690.0300.0650.0210.0000.0000.0720.0390.0200.0221.0000.000
Target0.0780.2160.0790.0390.1110.0970.0160.1250.1080.1180.1080.1060.2190.0450.1730.2580.4550.3830.0620.0420.1390.2740.5160.4520.0660.0530.0360.0520.0780.1120.0000.2410.4310.2290.3040.0001.000

Missing values

2023-11-04T09:31:56.028357image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-04T09:31:56.398801image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Marital StatusApplication modeApplication orderCourseDaytime/evening attendancePrevious qualificationPrevious qualification (grade)NacionalityMother's qualificationFather's qualificationMother's occupationFather's occupationAdmission gradeDisplacedEducational special needsDebtorTuition fees up to dateGenderScholarship holderAge at enrollmentInternationalCurricular units 1st sem (credited)Curricular units 1st sem (enrolled)Curricular units 1st sem (evaluations)Curricular units 1st sem (approved)Curricular units 1st sem (grade)Curricular units 1st sem (without evaluations)Curricular units 2nd sem (credited)Curricular units 2nd sem (enrolled)Curricular units 2nd sem (evaluations)Curricular units 2nd sem (approved)Curricular units 2nd sem (grade)Curricular units 2nd sem (without evaluations)Unemployment rateInflation rateGDPTarget
0117517111122.01191259127.310011020000000.000000000000.000000010.81.41.74Dropout
11151925411160.011333142.5100010190066614.0000000066613.666667013.9-0.30.79Graduate
2115907011122.01373799124.810001019006000.000000006000.000000010.81.41.74Dropout
31172977311122.01383753119.6100100200068613.42857100610512.40000009.4-0.8-3.12Graduate
42391801401100.01373899141.5000100450069512.3333330066613.000000013.9-0.30.79Graduate
523919991019133.11373797114.80011105000510511.85714300517511.500000516.20.3-0.92Graduate
6111950011142.011938710128.4100101180079713.3000000088814.345000015.52.8-4.06Graduate
71184925411119.01373799113.110001022005500.000000005500.000000015.52.8-4.06Dropout
8113923811137.0621199129.3000101211068613.8750000067614.142857016.20.3-0.92Graduate
9111923811138.0111947123.0101000180069511.40000000614213.50000008.91.43.51Dropout
Marital StatusApplication modeApplication orderCourseDaytime/evening attendancePrevious qualificationPrevious qualification (grade)NacionalityMother's qualificationFather's qualificationMother's occupationFather's occupationAdmission gradeDisplacedEducational special needsDebtorTuition fees up to dateGenderScholarship holderAge at enrollmentInternationalCurricular units 1st sem (credited)Curricular units 1st sem (enrolled)Curricular units 1st sem (evaluations)Curricular units 1st sem (approved)Curricular units 1st sem (grade)Curricular units 1st sem (without evaluations)Curricular units 2nd sem (credited)Curricular units 2nd sem (enrolled)Curricular units 2nd sem (evaluations)Curricular units 2nd sem (approved)Curricular units 2nd sem (grade)Curricular units 2nd sem (without evaluations)Unemployment rateInflation rateGDPTarget
4414111913011137.0133835129.3100100180056511.8000001058511.60000009.4-0.8-3.12Graduate
441543919500119133.11373766117.80010004600714312.33333300712311.083333011.10.62.02Dropout
44161432950011136.01383895131.30001002301114151212.62500011114151212.62500017.62.60.32Graduate
4417111907011132.011199133.8100101200066613.8333330066613.500000016.20.3-0.92Graduate
441814419070139120.0133839120.0000110200277612.50000005910713.142857116.20.3-0.92Graduate
4419116977311125.011154122.2000110190067513.6000000068512.666667015.52.8-4.06Graduate
4420112977311120.01051199119.0101000181066612.0000000066211.000000011.10.62.02Dropout
4421111950011154.01373799149.5100101300078714.9125000089113.500000013.9-0.30.79Dropout
4422111914711180.01373774153.8100101200055513.8000000056512.00000009.4-0.8-3.12Graduate
44231101977311152.022383759152.0100100221068611.6666670066613.000000012.73.7-1.70Graduate